What to Consider When Selecting the Right Machine Learning Platform

The market for machine learning platforms is evolving. You can see lots of machine learning platforms today than you would before. As technology is changing, more enterprises are embracing and implementing automated machine learning practices in their operations. It adds value to your bottom line, helps cut down operational costs, gains actionable insights from data, and improves your products or services. 

Machine learning is just one part of artificial intelligence. And, the base of it is algorithms. The algorithms are programs using labeled and unlabeled data sets. These trained algorithms can predict any new pattern of data using their input data. 

What Is The Need Of Machine Learning Platforms? 

Machine learning technology is fast evolving and needs skilled data scientists, which are less in number. The platform facilitates you with the infrastructure to run down your machine learning operations based on pre-built algorithms. It offers tools for the development, deployment, and improvement of various algorithms. It facilitates end-to-end machine learning allowing you to manage the entire data cycle right from the integration to the interpretation. 

How Does Machine Learning Platforms Work

The two basic categories of machine learning platforms are cloud and on-premise. Machine learning in the cloud is popular due to the flexibility it offers. Unlike the on-premise platform, the cloud platform doesn’t need on-site infrastructure. On the other hand, on-site premises are selected for security and speed. It can be located either at the business site or another location. Here is a list of a few things that you must consider while choosing machine learning platforms. 

Mistakes to Avoid When Selecting the Right Machine Learning Platform

• Not Keeping Data Close: Don’t let your data travel half the world and try to keep it close. Long-distance is related to latency. Try to build an automated machine learning model where data already resides, avoiding any mass data transmission. Keep your data on the same speed network as your model-building software. 

• Not Looking For Pipeline Configurations: ELT (export, load, and transform) and ETL (export, transform, and load) are the most common configurations used in the database world. These configurations help you reduce time during the load phase. Noisy data is best when filtered, and variables need standardization for machine learning. 
• Not Considering Scaling Up or Scale-Out Training: Scaling is an integral part of machine learning. It is about handling a considerable amount of data and performing functions effectively and efficiently. Hence, scalability is an important aspect when choosing a platform. As data is increasing, vertical scaling can be expensive. Training the model takes time, or big datasets might not fit into the training device’s existing memory; here, scalability will save you.

Look for a platform that supports transfer learning and offers pre-trained models. Training your models can be expensive and time-consuming. To save your time and energy, you look for platforms that provide pre-trained models. These models are useful to work with massive datasets. 

However, many times, these pre-trained models are incapable of identifying your objectives. In such a case, you should go for transfer learning as it replaces a few layers of neural network with your data without the need to train the complete network.